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Building the metaverse using digital twins at all scales,states,and relations

Virtual Reality & Intelligent Hardware 2022年第6期4卷 459-470页

作者： Zhihan LV Shuxuan XIE Yuxi LI M.Shamim HOSSAIN Abdulmotaleb El SADDIK Department of Game Design Faculty of ArtsUppsala UniversitySweden College of Computer Science and Technology Qingdao UniversityQingdaoChina Department of Software Engineering College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia School of Electrical Engineering and Computer Science(EECS) University of OttawaOttawaOntarioCanada

Developments in new-generation information technology have enabled Digital Twins to reshape the physical world into a virtual digital space and provide technical support for constructing the *** objects can be at the micro-,meso-,or *** Metaverse is a complex collection of solid,liquid,gaseous,plasma,and other uncertain ***,the Metaverse integrates tangibles with social relations,such as interpersonal(friends,partners,and family)and social relations(ethics,morality,and law).This review introduces some principles and laws,such as broken windows theory,small-world phenomenon,survivor bias,and herd behavior,for constructing a Digital Twins model for social ***,from multiple perspectives,this article reviews mappings of tangible and intangible real-world objects to the Metaverse using the Digital Twins model.

关键词： Digital twins Metaverse Sensor

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SDC-YOLO: A Method for Road Defect Detection

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2025年第6期52卷 1930-1939页

作者： Wu, Man Tao, Ye School of Computer and Software Engineering University of Science and Technology Liaoning Anshan 114051 China School of Computer and Software Engineering University of Science and Technology Liaoning Anshan 114051 China

To tackle the challenges associated with complex backgrounds, low-resolution images, and similar crack features that lead to missed detections, false positives, and low accuracy in pavement defect detection, we propose an enhanced pavement defect detection model named SDC-YOLO, which builds upon YOLOv8. Specifically, in the backbone and neck components, we replaced conventional convolution layers with SPDconv, an innovative approach that combines spatial-to-depth layers with non-strided convolution layers, thereby enhancing the model’s capability to extract small-scale features. Additionally, in the neck component, we incorporated the lightweight dynamic upsampling module (Dysample) as a replacement for conventional upsampling techniques, enriching feature details while reducing computational overhead. Furthermore, we developed the efficient CSC2f module to minimize redundant features, enhance multi-scale feature extraction and fusion capabilities, and reduce the parameter count and computational load. On the RDD2022 dataset, compared to the baseline model, the mAP@0.5 of SDC-YOLO has increased by 2.3%, while the number of parameters and FLOPs has seen a slight rise. The model consistently outperforms other algorithms, thereby providing robust validation of its effectiveness and superiority. © (2025), (International Association of Engineers). All Rights Reserved.

关键词： multi-scale features road defect detection upsampling YOLOv8

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Cyber Security Threats on Multifunctional Devices and Mitigation Techniques 2

Cyber Security Threats on Multifunctional Devices and Mitiga...

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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Al-Dmour, Nidal A. Farooq, Umer Sarwar, Irfan Iqbal, Muhammad Waseem Aqeel, Muhammad Khan, Wasim Ahmad Al Hamadi, Hussam Mutah University College of Engineering Department of Computer Engineering Jordan LGU Department of Computer Science Lahore Pakistan Superior University Department of Computer Science Lahore54000 Pakistan Superior University Department of Software Engineering Lahore54000 Pakistan NCBA&E Lahore School of Computer Science Pakistan Applied Science Research Center Applied Science Private University Amman11937 Jordan University of Dubai College of Engineering and IT PO Box 14143 United Arab Emirates

ISBN: (纸本)9798350335644

The risks discussed in this document are related to smart printers/ fax/ photocopiers/ or e-mail-enabled smart devices, also known as "document centers,". These devices can be purchased outright or rented every month. When an item reaches its end of life, it is either donated to a non-profit organization or returned to the manufacturer (OEM) or supplier at the end of a lease arrangement. These network-interconnected devices have an inherent susceptibility that most IT operations teams or information security professionals are unaware of. This effort will raise awareness of the dangers of losing a company's data. This discussion will focus on reported vulnerabilities, previous discussions, and given the author's view on the potential danger. Create instructions for potential users on how to use and disassemble these devices safely to prevent losing sensitive data. Lastly, the technological structure of two potential attack vectors against a document center of an organization will be demonstrated in this study. The study was conducted in government as well as the private sector, medium-sized enterprises (300 employees of each entity) settings, outside of normal business hours, with special authorizations and engagement, limiting the scope to a minimal set of devices. © 2023 IEEE.

关键词： Sensitive data

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Multiplex Networks and Pan-Cancer Multiomics-Based Driver Gene Identification Using Graph Neural Networks

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Big Data Mining and Analytics 2024年第4期7卷 1262-1272页

作者： Xingyi Li Junming Li Jun Hao Xingyu Liao Min Li Xuequn Shang School of Computer Science Northwestern Polytechnical UniversityXi’an 710072Chinaand with Research&Development Institute of Northwestern Polytechnical University in ShenzhenShenzhen 518063China School of Software Northwestern Polytechnical UniversityXi’an 710072Chinaand with Research&Development Institute of Northwestern Polytechnical University in ShenzhenShenzhen 518063China School of Computer Science Northwestern Polytechnical UniversityXi’an 710072China School of Computer Science and Engineering Central South UniversityChangsha 410083China

Identifying cancer driver genes has paramount significance in elucidating the intricate mechanisms underlying cancer development,progression,and therapeutic *** omics data and interactome networks provided by numerous extensive databases enable the application of graph deep learning techniques that incorporate network structures into the deep learning ***,most existing models primarily focus on individual network,inevitably neglecting the incompleteness and noise of ***,samples with imbalanced classes in driver gene identification hamper the performance of *** address this,we propose a novel deep learning framework MMGN,which integrates multiplex networks and pan-cancer multiomics data using graph neural networks combined with negative sample inference to discover cancer driver genes,which not only enhances gene feature learning based on the mutual information and the consensus regularizer,but also achieves balanced class of positive and negative samples for model *** reliability of MMGN has been verified by the Area Under the Receiver Operating Characteristic curves(AUROC)and the Area Under the Precision-Recall Curves(AUPRC).We believe MMGN has the potential to provide new prospects in precision oncology and may find broader applications in predicting biomarkers for other intricate diseases.

关键词： cancer driver gene multiplex networks pan-cancer multiomics data graph neural networks negative sample inference

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Framework for improving software Requirements via Automated Behavior-Driven Development: A Priority-Based Approach*

Framework for improving Software Requirements via Automated ...

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International Conference on Communication Technologies (ComTech)

作者： Zainab Yousaf Muhammad Sohail Javed Iqbal Asad Ullah Sumet Mehta Department of Computer Science Software Engineering National University of Science and Technology Islamabad Pakistan School of Computer Science and Communication Engineering Jiangsu University Zhenjiang Jiangsu China

ISBN: (数字)9798331515331

ISBN: (纸本)9798331515348

A novel framework for automating behavior-driven development (BDD) and improving software requirements is presented in this paper using a priority-based approach. Through the integration of BDD concepts with automated prioritization techniques, the framework seeks to address the difficulties associated with software demand prioritization. The main goal is to make sure that important features are addressed first by coordinating software development efforts with business priorities. The Priority Predictor, a crucial element that uses BDD scenarios to rank software needs, was designed and implemented as part of the framework’s development. The framework is simple to use in development processes since it interfaces smoothly with popular BDD tools like Cucumber.

关键词： software Communications technology software development management Business

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CRAYM: neural field optimization via camera RAY matching 24

CRAYM: neural field optimization via camera RAY matching

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Liqiang Lin Wenpeng Wu Chi-Wing Fu Hao Zhang Hui Huang College of Computer Science and Software Engineering Shenzhen University Department of Computer Science and Engineering The Chinese University of Hong Kong School of Computing Science Simon Fraser University

ISBN: (纸本)9798331314385

We introduce camera ray matching (CRAYM) into the joint optimization of camera poses and neural fields from multi-view images. The optimized field, referred to as a feature volume, can be "probed" by the camera rays for novel view synthesis (NVS) and 3D geometry reconstruction. One key reason for matching camera rays, instead of pixels as in prior works, is that the camera rays can be parameterized by the feature volume to carry both geometric and photometric information. Multi-view consistencies involving the camera rays and scene rendering can be naturally integrated into the joint optimization and network training, to impose physically meaningful constraints to improve the final quality of both the geometric reconstruction and photorealistic rendering. We formulate our per-ray optimization and matched ray coherence by focusing on camera rays passing through keypoints in the input images to elevate both the efficiency and accuracy of scene correspondences. Accumulated ray features along the feature volume provide a means to discount the coherence constraint amid erroneous ray matching. We demonstrate the effectiveness of CRAYM for both NVS and geometry reconstruction, over dense- or sparse-view settings, with qualitative and quantitative comparisons to state-of-the-art alternatives.

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Design of Evolutionary Algorithm Based Energy Efficient Clustering Approach for Vehicular Adhoc Networks

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computer Systems science & engineering 2023年第7期46卷 687-699页

作者： VDinesh SSrinivasan Gyanendra Prasad Joshi Woong Cho Department of Electronics and Communication Engineering Kongu Engineering CollegePerundurai638060India Institute of Biomedical Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesSaveetha University602105ChennaiIndia Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Software Convergence Daegu Catholic UniversityGyeongsan38430Korea

In a vehicular ad hoc network(VANET),a massive quantity of data needs to be transmitted on a large scale in shorter time *** the same time,vehicles exhibit high velocity,leading to more vehicle *** of these characteristics result in unreliable data communication in VANET.A vehicle clustering algorithm clusters the vehicles in groups employed in VANET to enhance network scalability and connection *** is considered one of the possible solutions for attaining effectual interaction in *** one such difficulty was reducing the cluster number under increasing transmitting *** article introduces an Evolutionary Hide Objects Game Optimization based Distance Aware Clustering(EHOGO-DAC)Scheme for *** major intention of the EHOGO-DAC technique is to portion the VANET into distinct sets of clusters by grouping *** addition,the DHOGO-EAC technique is mainly based on the HOGO algorithm,which is stimulated by old games,and the searching agent tries to identify hidden objects in a given *** DHOGO-EAC technique derives a fitness function for the clustering process,including the total number of clusters and Euclidean *** experimental assessment of the DHOGO-EAC technique was carried out under distinct *** comparison outcome stated the enhanced outcomes of the DHOGO-EAC technique compared to recent approaches.

关键词： Vehicular networks clustering evolutionary algorithm fitness function distance metric

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Research on Vehicle Detection Algorithms Based on YOLOv5s

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2023年第4期50卷 1453页

作者： Zhang, Lifeng Tian, Ying School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

To tackle the challenges associated with low recognition rates and demanding computational requirements in vehicle detection, we suggest a MAGS-YOLOv5s based vehicle detection model that builds on an upgraded version of YOLOv5s. Initial efforts are focused on applying data augmentation, using mosaic augmentation and integrating mix-up augmentation to enhance the model’s generalisation and robustness, thus increasing sample diversity. Secondly, the loss function for predicting bounding boxes is enhanced by including an α coefficient in addition to CIoU, facilitating adjustment to varying data scales and hastening the model’s convergence speed. Moreover, Ghost Shuffle Convolution (GSC) is employed to substitute the convolutional layers in the neck of the initial network model. This replacement decreases the computational load and network parameters, whilst enhancing the feature extraction capability and robustness of the network model. Our proposed SGC block, which is built on the GC block, has been integrated to the C3 module to specifically improve the feature extraction capability of the model for occluded object detection. Experimental results indicate that the enhanced network delivers a boost of 2.9% and 5.3% in mAP (mean average precision) on the Pascal VOC and MS COCO vehicle datasets, respectively, compared to the original model. The improved network displays enhanced multi-scale representation and generalisation capabilities. © 2023, International Association of Engineers. All rights reserved.

关键词： Feature extraction

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Audio-Text Multimodal Speech Recognition via Dual-Tower Architecture for Mandarin Air Traffic Control Communications

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computers, Materials & Continua 2024年第3期78卷 3215-3245页

作者： Shuting Ge Jin Ren Yihua Shi Yujun Zhang Shunzhi Yang Jinfeng Yang School of Computer Science and Software Engineering University of Science and Technology LiaoningAnshan114051China Institute of Applied Artificial Intelligence of the Guangdong-Hong Kong-Macao Greater Bay Area Shenzhen Polytechnic UniversityShenzhen518055China Shenzhen Institutes of Advanced Technology Chinese Academy of SciencesShenzhen518055China Industrial Training Centre Shenzhen Polytechnic UniversityShenzhen518055China

In air traffic control communications (ATCC), misunderstandings between pilots and controllers could result in fatal aviation accidents. Fortunately, advanced automatic speech recognition technology has emerged as a promising means of preventing miscommunications and enhancing aviation safety. However, most existing speech recognition methods merely incorporate external language models on the decoder side, leading to insufficient semantic alignment between speech and text modalities during the encoding phase. Furthermore, it is challenging to model acoustic context dependencies over long distances due to the longer speech sequences than text, especially for the extended ATCC data. To address these issues, we propose a speech-text multimodal dual-tower architecture for speech recognition. It employs cross-modal interactions to achieve close semantic alignment during the encoding stage and strengthen its capabilities in modeling auditory long-distance context dependencies. In addition, a two-stage training strategy is elaborately devised to derive semantics-aware acoustic representations effectively. The first stage focuses on pre-training the speech-text multimodal encoding module to enhance inter-modal semantic alignment and aural long-distance context dependencies. The second stage fine-tunes the entire network to bridge the input modality variation gap between the training and inference phases and boost generalization performance. Extensive experiments demonstrate the effectiveness of the proposed speech-text multimodal speech recognition method on the ATCC and AISHELL-1 datasets. It reduces the character error rate to 6.54% and 8.73%, respectively, and exhibits substantial performance gains of 28.76% and 23.82% compared with the best baseline model. The case studies indicate that the obtained semantics-aware acoustic representations aid in accurately recognizing terms with similar pronunciations but distinctive semantics. The research provides a novel modeling pa

关键词： Speech-text multimodal automatic speech recognition semantic alignment air traffic control communications dual-tower architecture

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Prediction of global mean temperature based on SARIMA combined model 4

Prediction of global mean temperature based on SARIMA combin...

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4rth International Conference on Applied Mathematics, Modelling, and Intelligent Computing, CAMMIC 2024

作者： Li, Dongliang Chu, Chengxu Gao, Huihui Yang, Chenming Liu, Xin Huang, Zhenwu School of Humanities and Law Southwest Forestry University Kunming650224 China School of Mechanical Engineering and Automation Dalian Polytechnic University Dalian116034 China Data Science and Intelligent Media School Communication University of China Beijing100024 China School of Computer and Software Engineering Xihua University Chengdu610039 China

ISBN: (纸本)9781510681637

With the continuous development of human society, the global warming caused by greenhouse gas emissions is becoming more and more serious. In order to better cope with global warming, this paper uses the SARIMA model to predict global temperature. Then, SARIMA (1,1,1) (1,1,1) model is established to forecast the temperature. It is found that the temperature in 2050 and 2100 will not reach 20℃, and the global average temperature is expected to reach 20℃ in about 2180. Through model evaluation, it is found that the R-square of SARIMA model is 0.87. © 2024 SPIE.

关键词： Global warming

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